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Post doc position in theory of machine learning at Department of Computer Science, Aarhus University
is on understanding and improving the performance of classic learning algorithms, in particular Boosting and Bagging, both in terms of speed and generalization capabilities. The project also allows
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methods may include physical models, multivariable analysis, self-diagnosis, and AI algorithms. Given the importance of long operating times for underwater sensors, energy-efficient processing is central to
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other PhD projects within SFI Smart Ocean. These methods may include physical models, multivariable analysis, self-diagnosis, and AI algorithms. Given the importance of long operating times for underwater
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contributions to standardisation efforts. Your role This fully funded PhD position focuses on exploring and developing hybrid quantum-classical algorithms to tackle sustainability challenges. Key areas include
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will design quantum-safe threshold encryption and/or authentication algorithms. The expected outcome is the design of methods, techniques and their software prototype to implement quantum-safe threshold
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modern Bayesian modelling frameworks such as Stan, Turing.jl, and PyMC, including automatic differentiation frameworks, MCMC sampling algorithms, and iterative Bayesian modelling. Special attention will be
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operations. The goal of this project is to conceive, develop, and evaluate optimization models and algorithms for increasing the economic efficiency of last-mile delivery operations, including routing
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novel opportunity to automate and improve the frailty assessment process, aiming for greater consistency and predictive accuracy. Aims i) Develop a deep learning algorithm to autonomously detect and
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control engineering, optimization algorithms Control of drones and flight experiments as well as knowledge in AI / Machine Learning would be an asset Outstanding academic records Teamworking experience, e.g
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sustainability challenges through research and active contributions to standardisation efforts. Your role This fully funded PhD position focuses on exploring and developing hybrid quantum-classical algorithms